EPOS-OHCA: Early Predictors of Outcome and Survival after non-traumatic Out-of-Hospital Cardiac Arrest

Background Post-cardiac arrest syndrome (PCAS) after out-of-hospital cardiac arrest (OHCA) poses significant challenges due to its complex pathomechanisms involving inflammation, ischemia, and reperfusion injury. The identification of early available prognostic indicators is essential for optimizing therapeutic decisions and improving patient outcomes. Methods In this retrospective single-center study, we analyzed real-world data from 463 OHCA patients with either prehospital or in-hospital return of spontaneous circulation (ROSC), treated at the Cardiac Arrest Center of the University Hospital of Marburg (MCAC) from January 2018 to December 2022. We evaluated demographic, prehospital, and clinical variables, including initial rhythms, resuscitation details, and early laboratory results. Statistical analyses included logistic regression to identify predictors of survival and neurological outcomes. Results Overall, 46.9% (n = 217) of patients survived to discharge, with 70.1% (n = 152) achieving favorable neurological status (CPC 1 or 2). Age, initial shockable rhythm, resuscitation time to return of spontaneous circulation (ROSC), and early laboratory parameters like lactate, C-reactive protein, and glomerular filtration rate were identified as independent and combined Early Predictors of Outcome and Survival (EPOS), with high significant predictive value for survival (AUC 0.86 [95% CI 0.82–0.89]) and favorable neurological outcome (AUC 0.84 [95% CI 0.80–0.88]). Conclusion Integration of EPOS into clinical procedures may significantly improve clinical decision making and thus patient prognosis in the early time-crucial period after OHCA. However, further validation in other patient cohorts is needed.


Introduction
One of the biggest challenges in intensive care medicine is the timecritical determination of the most appropriate therapy for critically ill patients.In out-of-hospital cardiac arrest (OHCA) patients, the outcome ultimately depends on resuscitation-related factors such as the initial heart rhythm and the duration of cardiopulmonary resuscitation (CPR).However, in these patients timely and accurate decision-making is critical due to the post-cardiac arrest syndrome https://doi.org/10.1016/j.resplu.Abbreviations: BMI, body mass index, CAC, cardiac arrest center, CHD, coronary heart disease, COPD, chronic obstructive pulmonary disease, CPC, Cerebral Performance Category, CPR, cardiopulmonary resuscitation, CRP, C-reactive protein, eCPR, extracorporeal cardiopulmonary resuscitation, GCS, Glasgow Coma Scale, GFR, glomerular filtration rate, KDIGO, Kidney Disease: Improving Global Outcomes (scoring system), MCAC, Marburg Cardiac Arrest Center, MI, myocardial infarction, mRS, modified Rankin Scale, OHCA, out-of-hospital cardiac arrest, OSAS, obstructive sleep apnea syndrome, PAE, pulmonary artery embolism, PCAS, post cardiac-arrest syndrome, PCT, procalcitonin, PEA, pulseless electrical activity, py, pack years, TTM, targeted temperature management, ROSC, return of spontaneous circulation, VF, ventricular fibrillation, VT, ventricular tachycardia, WLST, withdraw life-sustaining treatment * Corresponding author at: Philipps University of Marburg, University Hospital, Department of Cardiology, Angiology, and Intensive Care Medicine Baldingerstrasse 35043 Marburg.
(PCAS) that occurs after OHCA.][3] Thus, a key aspect of improving patient outcomes is the identification of early prognostic indicators that can guide clinical decisionmaking and tailor interventions to individual patient needs.The most optimal, highly specified, and specialized therapy should be offered according to clinical, ethical, and ultimately, socio-economic considerations.[9][10][11] Nowadays, in addition to the universally valid critical care scores such as Therapeutic Interventions Scoring System (TISS), Simplified Acute Physiology Score (SAPS) and Sequential Organ Failure Assessment (SOFA), new prognostic scoring systems such as Post-Cardiac Arrest Syndrome for Therapeutic Hypothermia (CAST) and Pittsburgh Cardiac Arrest Category (PCAC) have been defined.They all consider various factors and endpoints with a particular focus on neurological outcome at different time points of therapy after OHCA. 12,13The TISS and SAPS scores are primarily used to assess organ function and the extent of nursing care on the intensive care unit (ICU).In contrast, the CAST score and its simplified version (rCAST) are used early after OHCA for prognostic evaluation, originally to guide the initiation of targeted temperature management (TTM).Both, the CAST and rCAST scores have shown high accu-racy in predicting neurological prognosis and they are superior in validity to PCAC score. 12,14-16However, it is not yet widely used for basic treatment decisions, as the prognostic significance varies due to a non-generalized observation approach limited by rare parameters.
To support treatment decision-making in a time-critical situation, early available and easily applicable predictors of survival and neurological outcome were identified from five years of real-world data of post-resuscitation management at the Marburg University Hospital's Cardiac Arrest Center (MCAC) in Germany.

Setting
This retrospective, single-center study analyses data from patients admitted to the MCAC after OHCA from January 2018 to December 2022.This includes pre-hospital data from emergency medical services (EMS) and inpatient care data from local databases.From 2018 to 2020, data was only collected from patients referred to the MCAC by EMS in one county.In 2021 and 2022, patients from surrounding counties were also included, with all EMS using uniform standards and equipment.

Inclusion
Patients were included if they were !18 years old, had experienced non-traumatic cardiac arrest, received pre-hospital resuscitation by EMS, and achieved return of spontaneous circulation (ROSC).The analysis included the entire cohort, distinguishing between "survivors" (discharged alive) and "non-survivors" (died during hospitalization).

Study design
The rCAST score was calculated for patients with complete data sets of the parameters included in the score: initial cardiac arrest rhythm, time to ROSC, pH value and lactate from arterial blood gas analyses, and Glasgow Coma Scale (GCS) motor score.Neurological outcome was assessed using Cerebral Performance Category (CPC) and Modified Rankin Scale (mRS) at discharge, with CPC status 1/2 or mRS 0-3 defined as a "favorable" neurological outcome and CPC status 3-5 or mRS ! 4 defined as "poor" neurological outcome.
For each patient, the CPC/mRS score was determined by two independent intensive care physicians at the time of discharge from the hospital.

Analysis
IBM SPSS Statistics (v29), GraphPad Prism (v10) and Stata (v16) were used for statistical analysis.Continuous variables were summarized as mean ± SD or median and interquartile range (IQR).Independent samples t-tests with Satterthwaite correction for unequal variances compared continuous variables between "survivors" and "non-survivors".Chi-squared tests with Yates correction were used to analyze categorical variables and the Mann-Whitney U test was used for non-parametric data, directly comparing medians.The significance level was set at 0.05 for all tests.Logistic regression models were performed to identify independent predictors of early outcome among variables that showed statistically significant differences in the preliminary univariate analysis.Stata software was used to calculate the area under the receiver operating characteristic (ROC) curve (AUC) for each logistic regression model.DeLong test was used to compare differences between AUC of ROC curves.The AUC values, together with the corresponding ROC curves, were generated to assess the discriminatory ability of the models to effectively discriminate between the outcome of "survivors" and "non-survivors" based on the predictors and rCAST score.For each identified significant predictor, cut-off values were determined using Youden's index.

Ethics
The retrospective analysis was approved by the local Ethics Committee of the Philipps University of Marburg in accordance with the Declaration of Helsinki (ek_mr_14072021).

Results
564 patients with OHCA were referred to the MCAC.After excluding 101 patients due to incomplete data or failure to meet the inclusion criteria (Supplementary Table 1), data from 463 patients could be analyzed.The reasons for the resuscitation event within the included patient cohort are listed in Supplementary Table 2. 46.9% of the patients (n = 217) survived to be discharged home or to rehabilitation, while 53.1% (n = 246) died in hospital.Of the "non-survivors", 7.3% (n = 18) died in the emergency department.38.2% (n = 94) died within 24 h on the ICU due to the severity of their condition and 54.5% (n = 134) died more than 24 h after admission.
In this context, it should be mentioned that this time (24 h) is not decisive for the decision to withdraw life-sustaining treatment (WLST).The decision to WLST is usually made more than 72 h after the relevant diagnosis unless earlier intervention is required (e.g.due to cerebral perfusion failure).

Pre-hospital parameters of resuscitation and baseline laboratory parameters
Pre-hospital rhythm analysis showed that "non-survivors" had a lower incidence of shockable rhythm (22.0% vs. 64.1%,p < 0.001)  2).The median time from the time EMS arrived at the scene to the time transport to the hospital for further care was 48.0 min for "survivors" (IQR 38.0-56.0)and 53.5 min for "non-survivors" (IQR 41.0-65.0).
The study found that "non-survivors" received significantly higher dosages of epinephrine during resuscitation than survivors (4.00 [IQR 2.00-8.00]vs. 1.00 [IQR 0.00-4.00],p < 0.001).Higher doses in "non-survivors" correlated with prolonged resuscitation and fewer initial shockable rhythms, consistent with guidelines.Therefore, epinephrine dose was excluded from further regression analysis to identify early predictors.

Identification of early available predictors of outcome
To identify early independent predictors of outcome, the significant variables from the univariate analysis were analyzed using multiple regression, with two models shown in the Appendix (Supplementary Table 3).BMI was excluded because of its limited power and avail-ability.Age significantly predicted survival, whereas gender, chronic renal insufficiency, and previous cardiac arrest did not.Initial shockable rhythm and resuscitation of less than 20 min strongly correlated with survival.The use of chest compression devices showed a negative but not significant correlation.In a model excluding lactate, the initial pH correlated strongly with lactate and survival (OR 13.01 [95% CI 2.97-56.91],p < 0.001).Lactate levels from arterial blood gas analysis, higher baseline CRP values (natural log), and lower GFR values were negatively associated with survival.The six significant predictors finally identified are shown in Fig. 1.
To obtain the most suitable results, the data were further analyzed and statistically evaluated.The above-mentioned six variables were again included in a logistic regression analysis.In this logistic regression model, the categorical variable of resuscitation time was replaced by the continuous variable of time (min) to ROSC and values were very stable when GFR was adjusted for the presence of CKD (data not shown).Table 3 shows the model and the significant Early Predictors of Outcome and Survival (EPOS) after OHCA with an AUC of 0.86 for survival, indicating strong predictive power.
Cut-off values for predictors within the EPOS model essential for survival prediction were determined using Youden's index (Supplementary Table 4).These values include age at 67.5 years; time from resuscitation initiation to ROSC at 18.5 min; lactate concentration within one hour after admission at 8.2 mmol/L; CRP at 12.0 mg/L; and GFR at 60.5 mL/min.
To evaluate the EPOS model against the established rCAST score, ROC curves were compared for both, survival, and neurological outcomes (Fig. 2).The study showed that the EPOS model had better predictive accuracy (DeLong Test: p < 0.001) for overall survival (AUC 0.86) than the rCAST score (AUC 0.76).Similarly, the EPOS model outperformed the rCAST score for neurological outcome with an AUC of 0.84 compared to 0.76 (DeLong Test: p = 0.001).

Discussion
To further support treatment decisions, we here identified a combination of Early Predictors of Outcome and Survival after OHCA (EPOS-OHCA).A comprehensive analysis of various patient and resuscitation-related data has identified six parameters that, when used in combination, seem to be superior to known and established scores such as the rCAST score.In detail, EPOS contains demo-graphic data, resuscitation-associated parameters, and early laboratory parameters.8][19] Interestingly, our study also found that early lactate levels, as well as CRP and GFR, were independent predictors when measured within the first hour after hospital admission.Except for lactate, these markers have only been investigated to a limited extent in previous studies.However, they should not be neglected as they reflect inflammation, quality of perfusion, and organ function, and are therefore important indicators of the severity of PCAS after OHCA. 20,21n addition, the results of this study once again underline the prognostic importance of the quality of renal function and the impact of renal failure on treatment outcomes. 22,23ystemic inflammation, primarily driven by ischemia-reperfusion injury, is a critical pathophysiological disturbance in PCAS following OHCA.Recent studies have repeatedly investigated the role of inflammatory markers as prognostic biomarkers in PCAS, which is often described as "sepsis-like syndrome". 24,25Cytokines (e.g.Interleukin 6, Tumor necrosis factor a) and inflammatory markers, such as CRP and procalcitonin (PCT) are valuable in assessing the severity of post-cardiac arrest syndrome (PCAS).Elevated levels and their progression correlate with organ failure and poor outcomes.Although PCT and cytokines among other markers offer promise for predicting disease progression, they are not routinely measured and are not widely available, limiting their utility for early decisionmaking.However, according to our data, CRP as a routinely available acute-phase protein, may be useful for management and therapy control after an OHCA.Moreover, our analysis confirmed an excellent predictive power of CRP.In case of further validation, this suggests its inclusion in future prediction models and research on early laboratory parameters.
In contrast to the established rCAST score, our analysis did not find witnessed cardiac arrest or pH to be independent predictors.In addition, we did not analyze the motor score of the Glasgow coma scale (GCS) because it depends on the use and dosage of postresuscitation sedatives.Thus, in our opinion GCS may not accurately reflect the patient's baseline condition.These mentioned aspects suggest the possibility of greater variance in these parameters, potentially affecting the validity of the rCAST score.
However, it has to be pointed out, that outcome after OHCA is complex and influenced by multiple factors, including patient characteristics and the quality of pre-and in-hospital management.In this context, it is critical to clearly define not only pre-hospital management but also in-hospital treatment standards at CACs to optimize outcomes. 26Moreover, various pathologies cause cardiac arrest and in turn different complications that need to be identified and managed at an early stage during hospitalization.The successful treatment of these complications depends on the type and severity of the complication but ultimately has an impact on patient outcome.
In the absence of definitive scientific data, the process of prognostication should be based on a multimodal approach using cumulative data from therapy, including imaging (e.g., CT scans), functional testing (e.g., electroencephalogram), electrophysiology, hemodynamics, clinical observations, and biomarkers.
In addition, the EPOS-OHCA model integrates parameters that reflect the underlying principles and pathophysiologic challenges after OHCA and in PCAS, such as severity of inflammation (CRP) and indicators of systemic perfusion and organ function (time to ROSC, initial rhythm, lactate, GFR).
The clinical application of this combination of parameters, considering their cut-off values, may therefore provide further valuable support for prognosis and decision-making in the often time-critical situation following an OHCA.This includes not only decisions to WLST but also the immediate initiation of appropriate treatments such as mechanical circulatory support to optimize hemodynamics and perfusion, targeted temperature management, and antiinflammatory measures (e.g.immunoadsorption).

Limitations
Several limitations need to be considered when interpreting the results of our study.First, this study includes a single-center design, which may limit the generalizability of our results and affect the reliability of the universal application of the risk scores and predictors.Second, due to the retrospective nature of the observation and data analysis, further hidden confounding factors are possible.This refers to the limited validity of our results due to missing data and the consecutive need to adjust the cohort size accordingly.Third, only data from non-traumatic OHCA patients are included in the analysis.
Risk assessments are nevertheless essential for the direction of research.They can provide valuable information about measures to be taken.However, since their assessment and application are not yet fully understood due to, among other things, the multimodality of the situation and pathophysiology after OHCA, this should be done with appropriate care.To further confirm the results, there is a need for external validation in different clinical settings and cohorts.

Conclusions
In conclusion, the EPOS model is predictive of survival and favorable neurological outcome after non-traumatic OHCA in our single-center cohort.External validation is needed to determine whether the EPOS model can provide clinical decision support immediately after ROSC.

Fig. 2 -
Fig. 2 -Comparative analysis of the predictive power of the EPOS model and the rCAST score.Panel A shows ROC curves for survival comparing the EPOS model (blue curve, AUC 0.86 [95% CI 0.82-0.89])with the rCAST score (red curve, AUC 0.76 [95% CI 0.72-0.81]).Panel B shows ROC curves for neurological outcomes of the EPOS model (blue curve, AUC 0.84 [95% CI 0.80-0.88]vs. the rCAST score (red curve, AUC 0.76 [95% CI 0.72-0.81]).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 2 -
Pre-hospital resuscitation-associated and baseline laboratory parameters of "non-survivors" vs. "survivors".Baseline laboratory parameters were taken within the first hour after hospital admission.

Table 3 -
Early Predictors of Outcome and Survival (EPOS) after OHCA.